In conventional computing environments, query execution plans may be optimized for efficiency. Query execution plans are used by developers to meet various objectives, such as data access goals. In some scenarios, a developer may manage query optimization based on knowledge and experience. However, this technique is difficult to coordinate, significantly time consuming, and highly complex for managing code when attempting to explain internal operations and functions of a query optimizer. Further, the query optimizer does not typically allow a developer to specify procedures for calculating a desired result. Therefore, a need exists for providing efficient solutions for improved optimization of query execution plans generated in computing environments.